Passive sampling hypothesis did not shape microbial species–area relationships in open microcosm systems

Abstract The passive sampling hypothesis is one of the most important hypotheses used to explain the mechanism of species–area relationships (SAR) formation. This hypothesis has not yet been experimentally validated due to the confusion between passive sampling (a larger area may support more colonists when fully sampled) and sampling effects (more sampling effort will result in increased species richness when sampling is partial). In this study, we created an open microcosm system with homogeneous habitat, consistent total resources, and biodiversity background using Chinese paocai soup, a fermented vegetable, as a substrate. We made efforts to entirely exclude the influence of sampling effects and to exclusively obtain microorganisms from dispersal using microcosm and high‐throughput sequencing techniques. However, in this study, passive sampling based on dispersal failed to shape SAR, and community differences were predominantly caused by species replacement, with only minor contributions from richness differences. Ecological processes including extinction and competitive exclusion, as well as underlying factors like temporal scales and the small island effects, are very likely to have been involved in the studied system. To elucidate the mechanism of SAR development, future studies should design experiments to validate the involvement of dispersal independently.

developed on this basis is also an important mechanism for explaining SAR (Ben-Hur & Kadmon, 2020;Connor & McCoy, 2013;MacArthur et al., 1965;Ricklefs & Lovette, 1999;Williams et al., 1943); (2) The habitat diversity hypothesis contends that larger areas have more diverse habitats (Connor & McCoy, 2013); (3) the environmental filtering hypothesis contends that as abiotic selection intensifies, less adapted species on smaller islands are more likely to go extinct than those on larger islands (Chisholm et al., 2016;Liu et al., 2020); and (4) the passive sampling hypothesis contends that a larger area can accept more colonists (Ben-Hur & Kadmon, 2020;Connor & McCoy, 2013). Among them, the passive sampling hypothesis is the most controversial hypothesis to explain the SAR mechanism, it is even considered to be just an invalid hypothesis (Coleman et al., 1982;Cam et al., 2002;Ouin et al., 2006).
The passive sampling hypothesis is one of the main theories used to explain how SARs emerge (Burns et al., 2009;Cam et al., 2002;Gooriah et al., 2021;Yamaura et al., 2016). This hypothesis outlines an island species-area relationships (ISAR), which is an independent systematic sampling used to forecast the colonists on islands by stochastic dispersal from the mainland. It was first put forth by Connor and McCoy in 1979 based on islands. According to experts, there is a sizable population of species on the mainland that disperse at random to islands, with larger islands acquiring more colonists (Coleman et al., 1982;Connor & McCoy, 1979;Connor & McCoy, 2013) ( Figure 1a). According to these views, Passive sampling should be a distinct ecological process that emphasizes the reception of colonists from the species pool (Cam et al., 2002).
In subsequent research, the study of the passive sampling hypothesis is conducted only on the mainland (Kelly et al., 1989;Tangney et al., 1990). Species-area relationships have been ascribed to a sampling effect in continuous continental regions, i.e., phenomena in which the number of species observed grows with sampling effort when sampling is inadequate. Because the community usually contains many rare species which do not appear in all studied locations, even if their spatial distribution is random, they will only be gathered in larger areas (Wardle et al., 1999) (Figure 1b). Thus, while the sampling effect can generate monotonically increasing curves (Ouin et al., 2006;Preston et al., 1960), it is insufficient to generate the steeper slope of SAR curves (SARs). Such a curve is also not SARs (Cam et al., 2002;Ouin et al., 2006). In other words, in the absence of exhaustive detection of regional biodiversity, an increase in sampling area also implies an increase in sampling volume, which should result in species accumulation curves (SACs) shaped by sampling effects (Azovsky, 2011;Hui, 2008).
The difference between passive sampling and sampling effects is considerable. Passive sampling is the process of taking samples from a species pool as a result of random species dispersal (Burns et al., 2009;Guadagnin et al. 2009), whereas the sampling effect is the phenomenon in which species richness rises as sampling effort increases; it is just a product of sampling (a sampling artifact phenomenon) (Cam et al., 2002;Ouin et al., 2006;Rosenzweig, 1995).
Such a sampling artifact phenomenon is challenging to eliminate from continental sampling, making it impossible to quantify the importance of ecological processes like dispersal. Some researchers contend that the only factor contributing to increased species richness is a sampling impact unrelated to ecological processes (Ben-Hur & Kadmon, 2020;Bidwell et al., 2014). Some academics have also suggested that the correlation between species richness and sampling area may be the result of dispersal as well as sampling effects (Cam et al., 2002;Ouin et al., 2006;Storch et al., 2003). However, a large majority of people continue to believe that the sampling effects, not passive sampling, are what determine SAR (Ben-Hur & Kadmon, 2020;Bidwell et al., 2014).
According to the equilibrium theory of island biogeography, the passive sampling hypothesis emphasizes that ISAR is shaped by trade-offs among speciation, dispersal, and extinction, independent of sampling effects (MacArthur & Wilson, 2016). In fact, without eliminating sampling effects, SARs are not able to fully explain SAR and have much less theoretical and practical usefulness (Azovsky, 2011). The lack of a clear distinction between sampling effects and ecological processes in current SAR studies has complicated the investigation of the mechanisms behind SAR creation.
Therefore, the original presumption that more species can spread into a greater area should be used to validate the passive sampling hypothesis. Experiments must clearly define the methods by which passive sampling affects SAR by concentrating on the role of dispersal after accounting for sampling effects and ecological processes (Gooriah & Chase, 2020;Gooriah et al., 2021).
Microbial microcosm systems are an excellent way to construct a system for studying species-area relationships (Deng et al., 2021;Deng et al., 2022). In this study, an open microcosm system was created utilizing equal parts of homogeneously mixed paocai soup from the same period and open beakers. High-throughput sequencing of 16 S and ITS amplicon fragments from the system was then used to conduct SAR investigations. It posed to explore the microbial SAR shaping by passive sampling. We hypothesize that after the sampling effect is completely removed, the species richness will also increase significantly with the increase in passive sampling area, and SARs can be constructed.

| Experimental design
Microcosm systems were established using beakers with different opening sizes, and well-mixed and sterilized paocai soup (Figure 1c).
The same period of paocai soup in microbial microcosm research systems ensures a consistent starting point for microbial colonization, a well-mixed paocai soup controls for the effects of habitat heterogeneity and biodiversity context, and equal amounts of paocai soup ensures a consistent total amount of resources in each microcosm. Additionally, the increasingly sophisticated high-throughput sequencing technology provides the opportunity to eliminate sampling effects. To start, the experiment's beaker volume is just the right size to allow for thorough sampling. Secondly, to conduct our research at a finer amplified subsequence variants (ASVs) categorization level, we used high-throughput sequencing technologies. This guarantees that our monitoring is rather thorough. By controlling for factors other than area to be consistent and only gradually increasing the beaker opening area while placing the entire microcosm in the open space for passive microbial sampling. The aforementioned experimental system was able to meet the requirements for testing the passive sampling hypothesis.

| Handling of paocai soup
We filtered the paocai soup through sterile gauze to obtain paocai soup. The filtered paocai soup was sealed and precipitated for 12 h.
The supernatant was left, the precipitate was removed, and the paocai soup was repeatedly filtered 2-3 times to obtain a homogeneous texture of paocai soup. The treated paocai soup was then sterilized F I G U R E 1 Schematic diagram of the hypothesis. (a): Schematic diagram of the passive sampling hypothesis. The arrows represent the dispersal process, the yellow areas represent the mainland species pool, the blue areas represent small and larger islands respectively, and the patterns on the areas represent different species. (b): Schematic diagram of nested sampling. The different colored areas represent sampling areas, the patterns on the areas represent different species, and the different thick and thin arrows represent species richness. (c): Passive sampling experimental design diagram. The yellow area at the top represents the pool of microbial species from which all the dispersal species come; the black arrows of different thicknesses represent the amount of dispersal that can be accepted for different passive sampling areas, generally, the larger the area, the more colonists be received; the blue area in the six equal-sized beakers represents the 4000 mL of paocai soup added, The grey areas on the beaker represent the passive sampling areas, increasing from left to right.
to ensure a consistent background of microbial diversity and environmental heterogeneity.

| Microbiological sampling
A total of six identical sized beakers (5000 ml) were used, each with an equal amount (4000 ml) of paocai soup. A hole punch was used to punch holes in the lids of the beakers and the passive sampling area was controlled by controlling the number of holes punched, resulting in passive sampling areas of 0.25π cm 2 , 1.25π cm 2 , 12.5π cm 2 , 25π cm 2 , 37.5π cm 2 , and 50π cm 2 , respectively (We using the na1-6 represent the passive sampling areas from 0.25π to 50 cm 2 ). After all microcosms were made, it was sealed with sterile newspaper and initial denaturation at 95°C for 3 min, 27 cycles (95°C for 30 s, annealing at 55°C for 30 s, and extension at 72°C for 45 s), and final extension at 72°C for 10 min. The PCR procedure for the ITS1-1F region was as follows: initial denaturation at 98°C for 1 min, 30 cycles (denaturation at 98°C for 10 s, annealing at 50°C for 30 s, and extension at 72°C for 30 s), and final extension at 72°C for 10 min to achieve complete amplification of the target gene.

| Data processing
Raw data FASTQ files were imported into the QIIME tool, and sequences from each sample were identified, quality filtered, trimmed, denoised, and merged using the QIIME2 dada2 plugin, and used to obtain feature tables of ASVs (Bolyen et al., 2019;Callahan et al., 2016;Nearing et al., 2018). Next, the QIIME2 feature-classifier plugin was applied to match representative sequences of ASVs to the pre-trained version 13_8 99% similarity GREEN GENES database (trimming the database to the region V3-V4 based on 338F/806R primer pairs for bacteria) (DeSantis et al., 2006) and UNITE database (for fungi) to generate the taxonomy table (Bokulich et al., 2018).
All contaminating mitochondria and chloroplast sequences were subsequently eliminated using the QIIME2 feature-table plugin, excluding rare ASVs that accounted for less than 0.001% of the total sequences (Callahan et al., 2016;Marizzoni, 2020).

| Data analysis
All analyses involving the R language were done using R-4.2.1 (https:// cran.r-proje ct.org/src/base/R-4.2.1). Using the vegan package in R (https://github.com/vegan devs/vegan; Oksanen et al., 2012), species rarefaction curves were plotted using the sequencing depth as the horizontal coordinate and ASV as the vertical coordinate. To show the diversity and structure of the microbial community, we used a hierarchical stacking diagram. And we draw the bar graph with the bar graph function in R, the passive sampling area is represented as the horizontal coordinate, and the paocai soup in the microcosm serves as the vertical coordinates to show the case of the microcosmic pH with different sampling areas. The number of ASVs for each sample was calculated from the feature table, and this number was the sample richness. SARs were plotted with the log-transformed area as the horizontal coordinate and log-transformed species richness as the vertical coordinate, using linearly fitted P-values and slope values to determine whether there were significant species-area relationships. In addition, we calculated a linear correlation between logtransformed species richness and microcosmic pH. Finally, β-diversity partition was performed using the R language adespatial package (https://CRAN.R-proje ct.org/packa ge=adesp atial; Shen et al., 2020), and we used vegan, ggplot2 (https://ggplo t2.tidyv erse.org), and ggrepel packages (https://CRAN.R-proje ct.org/packa ge=ggrepel) for redundancy analysis (RDA) (Forester et al., 2018).

| RE SULTS
As the depth of sequencing increased, the species rarefaction curves first increased and then rapidly leveled off (Figure 2a The pH of paocai soup was 3.31, 3.48, 3.29, 3.27, 3.26, and 3.19 for the different microcosm systems. pH was greatest for the microcosm with an area of 1.25π cm 2 and least for the microcosm with an area of 50π cm 2 (Figure 3). Regression analysis showed no significant correlation between microbial richness and pH for paocai soup (p = .3359, Slope = −4.45). It showed no significant correlation between bacterial richness and pH (p = .3134, Slope = −5.114). And it showed no significant correlation between fungal richness and pH (p = .8944, Slope = .747).
There was no significant increase in species richness as the passive sampling area was changed. The overall microbial community did not show a significant SAR with increasing areas (Figure 4a: Slope = −.0224, R 2 = .0291, p = .7466). The bacterial community did not show a significant SAR as the area increased (Figure 4b: Slope = −.0100, R 2 = .0204, p = .7873). The fungal community did F I G U R E 2 Sequencing signature map. (a) species rarefaction curves. The different colored curves represent different samples, which was used to assess the saturation of the sample size. The rarefaction curves obtained species richness at different sequencing depths by performing 2000 resampling at some sequencing depth. Each point on the curve represents the mean species richness at resampling, the error line is the standard error, and the dashed line indicates the saturation of the sample sequencing volume. (b) Stacking map at the genus level. The abscissa represents the different samples (We using the na1-6 represent the passive sampling areas from 0.25π to 50 cm 2 ), with the ordinate being the relative richness, where the colored blocks show the relative richness of the dominant genus in the microcosm.

F I G U R E 3
Variation in microcosmic pH by area. The horizontal coordinate represents the passive sampling area in cm 2 and the vertical coordinate represents the pH of paocai soup in the microcosm.
To clarify the effect of different passive sampling areas on differences in microbial community composition in paocai soup, the β-diversity of microbial communities was decomposed into species replacement and richness differences. The results of the β-diversity partition analysis showed that the differences in bacterial community composition in all areas were dominated by the species replacement process, contributing 64.1%, while the richness difference process contributed relatively little to the β-diversity, contributing 17.9% (Figure 5b). Variation in fungal community composition across all areas was also dominated by species replacement processes, contributing 36.6%, while richness differences processes contributed a relatively small 17.8% to β-diversity (Figure 5c). Thus, the variation in the microcosm system was mainly due to species replacement, with a relatively small contribution from richness differences (Figure 5a).
RDA was used to analyze species richness concerning environmental factors in a constrained manner, and the results showed that the two groups diverged. Open area contributed significantly to the second axis and dominated the between-group variation. pH contributed significantly to the first axis and dominated the withingroup variation. However, the overall axis was less well explained at 30.8% (Figure 6).

| DISCUSS ION
The microcosm system used in this work ensured thorough sampling while higher-resolution ASVs were used to estimate microbial diversity (Nearing et al., 2018). As the depth of the sequencing increases, the rarefaction curves rise and then quickly plateau, showing that microbial diversity monitoring is feasible, according to the rarefaction curves. We also made sure that the microorganisms only come from dispersal by using sterilized paocai soup. All of this makes sure that the sampling effect is eliminated, leaving only the dispersal effect. In this experimental system, however, there was no discernible relationship between species richness and the passive sampling area, and the dispersal effect had no impact on SAR. Any one of four explanations could account for this outcome.
Firstly, other ecological processes in the system obscured the shaping effect of dispersal on SAR after microbial dissemination.
β-diversity partition showed that species replacement was the main cause of community variations, with little contribution from species richness difference (Carvalho et al., 2013). The same number of biological niches were provided by the system in this experiment, and arriving bacteria had to occupy these finite spaces.
The new species went in and only replaced the previous species once the ecological niches were filled, hence there was neither an increase nor a loss in the diversity of species. Furthermore, after microbial dispersal, extinction mechanisms are concealed (Niu et al., 2009;Vandermeer, 1972). Most common environmental microorganisms can grow in a neutral environment, and environmental microorganisms have varying degrees of tolerance to salt concentration and pH in paocai soup, with eventually tolerant species easily surviving the system and sensitive species frequently struggling to survive (Murphy et al., 2006). The paocai soup in this study shaped the microcosm, which has an extreme environment with a pH of 3.19-3.48 and high salinity. These findings indicate that, even though pH did not dominate the samples, high-salinity and low-pH microcosms have been selected for dispersal species.
Species extinction occurs in the microcosm system due to both of these mechanisms of action. Perhaps the larger the area in the current study system has a higher rate of microbial dispersal, but the system's extinction process obscures this role. As a result, when there is only one effect of dispersal in the system, passive sampling may be able to shape significant SAR.
Secondly, microcosms with smaller passive sampling areas provide more space for reproduction at different temporal scales under the same environmental conditions, resulting in greater microbial richness in microcosms with smaller sampling areas. In this experiment, equal amounts of sterile paocai soup were used to establish a consistent microcosm background while keeping the microcosm size constant.
Microorganisms entering the microcosm at different passive sampling sites face the same level of abiotic selection due to identical environmental conditions (Chisholm et al., 2016;Liu et al., 2020). Because there would be fewer species in limited areas, those that have adapted to the environment and survived will have greater space to reproduce.
The number of species continues to grow with temporal scales.
Thirdly, the trade-offs among speciation, extinction, and dispersal, three critical processes that change dynamically over temporal scales, impact the emphasis in island biogeography on species-area relationships (MacArthur & Wilson, 2016). Because of microorganisms' short life cycles, rapid community changes, and faster dynamics of their three fundamental processes, microbial SAR may not be constant over temporal scales. It also led us to observe no SAR.
Finally, the small island effect is expected to have an impact on SAR (Gao & Perry, 2016;Sfenthourakis et al., 2010;Triantis et al., 2012). The small island effect, which is consistent with the results of this experiment, is a phenomenon in which species richness is not significantly related to island size or grows at a slower rate than larger islands when island size falls below a certain threshold. Future research should include additional samples and island size gradients in their experimental design to further establish the significance of the small island effect.
If the SAR is still not present after excluding the above factors, then it indicates that the passive sampling hypothesis is just a pure sampling effect. In addition, there is some risk of chance that only one independent microcosm was set up for each area in this experiment. In future studies, we need to redesign a microcosm system that distinguishes microbial extinction from dispersal and explore the shaping of the passive sampling hypothesis on microbial SAR with a guaranteed sufficient sample size. F I G U R E 5 Microbial microcosm system β-diversity partition diagram (a) all microbial β-diversity partition in the microcosm system. (b) all bacterial β-diversity partition in the microcosm system. (c) all fungal β-diversity partition in the microcosm system. Triangular plots of β-diversity partition result. Each black dot represents a pair of samples. Their positions were determined by a triplet of values from the species similarity (Similarity), species replacement (Repl), and species richness differences (RichDif); each triplet sums to 1. The large circular dot in each graph is the centroid of the points; the larger black dots represent the mean values of the Similarity, Repl, and RichDif.

F I G U R E 6
We using the na1-6 represent the passive sampling areas from 0.25π to 50π cm 2 . Redundancy analysis Microcosms na1, na2, and na3 are the group I; microcosms na4, na5, and na6 are group II. Arrows indicate environmental factors and circles are 95% confidence intervals.

CO N FLI C T O F I NTE R E S T
The authors declare no competing or financial interests.

DATA AVA I L A B I L I T Y S TAT E M E N T
Chinese Paocai microbiome ITS and 16S amplicon sequencing data are released (GSA: CRA008326, CRA008330). Please access it from the following link: https://bigd.big.ac.cn/gsa/brows e/CRA00 8330 and https://bigd.big.ac.cn/gsa/brows e/CRA00 8326.